Adversarial-based UDA introduces a discriminator to adversarially learn domain-invariant representations by obfuscating domain-specific information. DANN [1]} introduced a gradient inversion layer and a domain discriminator to confuse the feature distributions of the two domains. CDAN [2]} further introduced categorical information entropy into the domain discriminator to alleviate the class mismatch problem. In order to maintain the inter-class discriminativeness while confusing the domain feature distribution, [3]} proposed a penalty method for penalizing the largest singular value. [4]} adopted a new adversarial paradigm, where the adversarial way occurs between the feature extractor and the classifier, rather than between the feature extractor and the domain discriminator. Such methods take the features within a domain as a whole and force the feature extractor to learn domain-invariant representation by confusing the domain discriminator. However, finer intra-domain information, such as class-specific features may be confused, thus reducing the discriminability.
